Insights into Energy Transfer in Light-Harvesting Complex II Through Machine-Learning Assisted Simulations.

Light-harvesting complex II (LHCII) is the major antenna of higher plants. Energy transfer processes taking place inside its aggregate of chlorophylls have been experimentally investigated with time-resolved techniques, but a complete understanding of the most relevant energy transfer pathways and relative characteristic times remains elusive. Theoretical models to disentangle experimental data in LHCII have long been challenged by the large size and complex nature of the system. Here, we show that a fully first-principles approach combining molecular dynamics and machine learning can be successfully used to reproduce transient absorption spectra and characterize the EET pathways and the involved times.

Predicting Solvatochromism of Chromophores in Proteins through QM/MM and Machine Learning.

Solvatochromism occurs in both homogeneous solvents and more complex biological environments, such as proteins. While in both cases the solvatochromic effects report on the surroundings of the chromophore, their interpretation in proteins becomes more complicated not only because of structural effects induced by the protein pocket but also because the protein environment is highly anisotropic. This is particularly evident for highly conjugated and flexible molecules such as carotenoids, whose excitation energy is strongly dependent on both the geometry and the electrostatics of the environment. Here, we introduce a machine learning (ML) strategy trained on quantum mechanics/molecular mechanics calculations of geometrical and electrochromic contributions to carotenoids' excitation energies. We employ this strategy to compare solvatochromism in protein and solvent environments. Despite the important specifities of the protein, ML models trained on solvents can faithfully predict excitation energies in the protein environment, demonstrating the robustness of the chosen descriptors.

Electronic Excited States from Physically Constrained Machine Learning.

Data-driven techniques are increasingly used to replace electronic-structure calculations of matter. In this context, a relevant question is whether machine learning (ML) should be applied directly to predict the desired properties or combined explicitly with physically grounded operations. We present an example of an integrated modeling approach in which a symmetry-adapted ML model of an effective Hamiltonian is trained to reproduce electronic excitations from a quantum-mechanical calculation. The resulting model can make predictions for molecules that are much larger and more complex than those on which it is trained and allows for dramatic computational savings by indirectly targeting the outputs of well-converged calculations while using a parametrization corresponding to a minimal atom-centered basis. These results emphasize the merits of intertwining data-driven techniques with physical approximations, improving the transferability and interpretability of ML models without affecting their accuracy and computational efficiency and providing a blueprint for developing ML-augmented electronic-structure methods.

How the pH Controls Photoprotection in the Light-Harvesting Complex of Mosses.

In response to varying light conditions, light-harvesting complexes (LHCs) switch from a light-harvesting state to a quenched state to protect the photosynthetic organism from excessive light irradiation in a strategy known as nonphotochemical quenching (NPQ). NPQ is activated by an acidification of the thylakoid lumen, which is sensed directly or indirectly by the LHC, resulting in a conformational change of the complex that leads to the quenched state. The conformational changes responsible for NPQ activation and their connection to specific quenching mechanisms are still unknown. Here, we investigate the pH-triggered conformational changes in the light-harvesting complex stress-related (LHCSR) of mosses. By combining constant-pH molecular dynamics and enhanced sampling techniques, we find that the pH sensitivity of the complex is driven by the coupled protonation of three residues modulating the conformation of the short amphipathic helix placed at the lumen side of the embedding membrane. Combining these results with quantum mechanics/molecular mechanics calculations, we show that the quenching mechanism sensitive to the pH goes through a charge-transfer between a carotenoid and an excited chlorophyll, which is controlled by the protein conformation.

Machine Learning Exciton Hamiltonians in Light-Harvesting Complexes.

We propose a machine learning (ML)-based strategy for an inexpensive calculation of excitonic properties of light-harvesting complexes (LHCs). The strategy uses classical molecular dynamics simulations of LHCs in their natural environment in combination with ML prediction of the excitonic Hamiltonian of the embedded aggregate of pigments. The proposed ML model can reproduce the effects of geometrical fluctuations together with those due to electrostatic and polarization interactions between the pigments and the protein. The training is performed on the chlorophylls of the major LHC of plants, but we demonstrate that the model is able to extrapolate well beyond the initial training set. Moreover, the accuracy in predicting the effects of the environment is tested on the simulation of the small changes observed in the absorption spectra of the wild-type and a mutant of a minor LHC.

A fast method for electronic couplings in embedded multichromophoric systems.

Electronic couplings are key to understanding exciton delocalization and transport in natural and artificial light harvesting processes. We develop a method to compute couplings in multichromophoric aggregates embedded in complex environments without running expensive quantum chemical calculations. We use a transition charge approximation to represent the quantum mechanical transition densities of the chromophores and an atomistic and polarizable classical model to describe the environment atoms. We extend our framework to estimate transition charges directly from the chromophore geometry, i.e., bypassing completely the quantum mechanical calculations using a regression approach. The method allows to rapidly compute accurate couplings for a large number of geometries along molecular dynamics trajectories.

The atomistic modeling of light-harvesting complexes from the physical models to the computational protocol.

The function of light-harvesting complexes is determined by a complex network of dynamic interactions among all the different components: the aggregate of pigments, the protein, and the surrounding environment. Complete and reliable predictions on these types of composite systems can be only achieved with an atomistic description. In the last few decades, there have been important advances in the atomistic modeling of light-harvesting complexes. These advances have involved both the completeness of the physical models and the accuracy and effectiveness of the computational protocols. In this Perspective, we present an overview of the main theoretical and computational breakthroughs attained so far in the field, with particular focus on the important role played by the protein and its dynamics. We then discuss the open problems in their accurate modeling that still need to be addressed. To illustrate an effective computational workflow for the modeling of light harvesting complexes, we take as an example the plant antenna complex CP29 and its H111N mutant.

A different perspective for nonphotochemical quenching in plant antenna complexes.

Light-harvesting complexes of plants exert a dual function of light-harvesting (LH) and photoprotection through processes collectively called nonphotochemical quenching (NPQ). While LH processes are relatively well characterized, those involved in NPQ are less understood. Here, we characterize the quenching mechanisms of CP29, a minor LHC of plants, through the integration of two complementary enhanced-sampling techniques, dimensionality reduction schemes, electronic calculations and the analysis of cryo-EM data in the light of the predicted conformational ensemble. Our study reveals that the switch between LH and quenching state is more complex than previously thought. Several conformations of the lumenal side of the protein occur and differently affect the pigments' relative geometries and interactions. Moreover, we show that a quenching mechanism localized on a single chlorophyll-carotenoid pair is not sufficient but many chlorophylls are simultaneously involved. In such a diffuse mechanism, short-range interactions between each carotenoid and different chlorophylls combined with a protein-mediated tuning of the carotenoid excitation energies have to be considered in addition to the commonly suggested Coulomb interactions.

The energy transfer model of nonphotochemical quenching: Lessons from the minor CP29 antenna complex of plants.

Antenna complexes in photosystems of plants and green algae are able to switch between a light-harvesting unquenched conformation and a quenched conformation so to avoid photodamage. When the switch is activated, nonphotochemical quenching (NPQ) mechanisms take place for an efficient deactivation of excess excitation energy. The molecular details of these mechanisms have not been fully clarified but different hypotheses have been proposed. Among them, a popular one involves excitation energy transfer (EET) from the singlet excited Chls to the lowest singlet state (S1) of carotenoids. In this work, we combine such model with µs-long molecular dynamics simulations of the CP29 minor antenna complex to investigate how conformational fluctuations affect the electronic couplings and the final EET quenching. The computational framework is applied to both CP29 embedding violaxanthin and zeaxantin in its L2 site. Our results demonstrate that the EET model is rather insensitive to physically reasonable variations in single chlorophyll-carotenoid couplings, and that very large conformational changes would be needed to see the large variation of the complex lifetime expected in the switch from light-harvesting to quenched state. We show, however, that a major role in regulating the EET quenching is played by the S1 energy of the carotenoid, in line with very recent spectroscopy experiments.